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Predicting the Drug–Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation by Hong-can Ren; Yang Sai; Tao Chen; Chun Zhang; Lily Tang; Cheng-guang Yang is a Medicine article available to read on EtoBox.

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The prediction of drug-drug interactions (DDIs) plays critical roles for the estimation of DDI risk caused by inhibition of CYP3A4. The aim of this paper is to develop a physiologically based pharmacokinetic (PBPK)-DDI model for prediction of the DDI co-administrated with ketoconazole in humans and evaluate the predictive performance of the model. The pharmacokinetic and biopharmaceutical properties of 35 approved drugs, as victims, were collected for the development of a PBPK model, which were linked to the PBPK model of ketoconazole for the DDI prediction. The PBPK model of victims and ketoconazole were validated by matching actual in vivo pharmacokinetic data. The predicted results of DDI were compared with actual data to evaluate the predictive performance. The percentage of predicted ratio of AUC (AUCR), C max (C max R), and T max (T max R) was 75%, 69%, and 91%, respectively, which were within the twofold threshold (range, 0.5-2.0×) of the observed values. Only 3% of the predicted AUCRs are obviously underestimated. After integration of the reported fraction of metabolism (f m ) into the PBPK-DDI model for limited four cases, the model-predicted AUCRs were improved from the t

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Author

Hong-can Ren; Yang Sai; Tao Chen; Chun Zhang; Lily Tang; Cheng-guang Yang

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Medicine (Life Sciences)